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Exploiting Core Criticality for Enhanced GPU Performance., , , , , , and . SIGMETRICS, page 351-363. ACM, (2016)NEBULA: A Neuromorphic Spin-Based Ultra-Low Power Architecture for SNNs and ANNs., , , , , , , , and . ISCA, page 363-376. IEEE, (2020)Opportunistic computing in GPU architectures., , , , , , , and . ISCA, page 210-223. ACM, (2019)CASH: compiler assisted hardware design for improving DRAM energy efficiency in CNN inference., , , , , and . MEMSYS, page 396-407. ACM, (2019)Quantifying Data Locality in Dynamic Parallelism in GPUs., , , , , and . SIGMETRICS (Abstracts), page 25-26. ACM, (2019)Measuring and modeling on-chip interconnect power on real hardware., , , , , and . IISWC, page 23-33. IEEE Computer Society, (2016)Anatomy of GPU Memory System for Multi-Application Execution., , , , , , , , and . MEMSYS, page 223-234. ACM, (2015)ScaleDNN: Data Movement Aware DNN Training on Multi-GPU., , , , , and . ICCAD, page 1-9. IEEE, (2021)Scheduling Techniques for GPU Architectures with Processing-In-Memory Capabilities., , , , , , , and . PACT, page 31-44. ACM, (2016)Distilling the Essence of Raw Video to Reduce Memory Usage and Energy at Edge Devices., , , , , and . MICRO, page 657-669. ACM, (2019)